Overview

Dataset statistics

Number of variables83
Number of observations278022
Missing cells7872212
Missing cells (%)34.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory176.1 MiB
Average record size in memory664.0 B

Variable types

Numeric9
Categorical73
Text1

Alerts

NPCIP11A has constant value ""Constant
NPCIP11B has constant value ""Constant
NPCIP11C has constant value ""Constant
NPCIP11D has constant value ""Constant
NPCIP13A1 has constant value ""Constant
NPCIP13B1 has constant value ""Constant
NPCIP14A has constant value ""Constant
NPCIP14B has constant value ""Constant
NPCIP17A has constant value ""Constant
NPCIP17B has constant value ""Constant
NPCIP17C has constant value ""Constant
NPCIP17D has constant value ""Constant
NPCIP17E has constant value ""Constant
NPCIP17F has constant value ""Constant
NPCIP17G has constant value ""Constant
NPCIP17H has constant value ""Constant
NPCIP17I has constant value ""Constant
NPCIP17J has constant value ""Constant
NPCIP17K has constant value ""Constant
NPCIP17L has constant value ""Constant
NPCIP17M has constant value ""Constant
DIRECTORIO is highly overall correlated with DIRECTORIO_HOG and 1 other fieldsHigh correlation
DIRECTORIO_HOG is highly overall correlated with DIRECTORIO and 1 other fieldsHigh correlation
DIRECTORIO_PER is highly overall correlated with DIRECTORIO and 1 other fieldsHigh correlation
NPCIP1 is highly overall correlated with NPCIP6A and 1 other fieldsHigh correlation
NPCIP12 is highly overall correlated with NPCIP12A and 2 other fieldsHigh correlation
NPCIP12A is highly overall correlated with NPCIP12 and 1 other fieldsHigh correlation
NPCIP12B is highly overall correlated with NPCIP12 and 3 other fieldsHigh correlation
NPCIP14 is highly overall correlated with NPCIP12High correlation
NPCIP16D is highly overall correlated with NPCIP12B and 1 other fieldsHigh correlation
NPCIP16F is highly overall correlated with NPCIP16GHigh correlation
NPCIP16G is highly overall correlated with NPCIP16F and 1 other fieldsHigh correlation
NPCIP16H is highly overall correlated with NPCIP16IHigh correlation
NPCIP16I is highly overall correlated with NPCIP16G and 1 other fieldsHigh correlation
NPCIP2B is highly overall correlated with NPCIP3 and 2 other fieldsHigh correlation
NPCIP2C is highly overall correlated with NPCIP7CHigh correlation
NPCIP3 is highly overall correlated with NPCIP2B and 3 other fieldsHigh correlation
NPCIP4 is highly overall correlated with NPCIP12B and 1 other fieldsHigh correlation
NPCIP6A is highly overall correlated with NPCIP1High correlation
NPCIP6B is highly overall correlated with NPCIP1High correlation
NPCIP7B is highly overall correlated with NPCIP2B and 2 other fieldsHigh correlation
NPCIP7C is highly overall correlated with NPCIP2CHigh correlation
NPCIP8D is highly overall correlated with NPCIP8DE and 2 other fieldsHigh correlation
NPCIP8DE is highly overall correlated with NPCIP8D and 1 other fieldsHigh correlation
NPCIP8E is highly overall correlated with NPCIP3 and 4 other fieldsHigh correlation
NPCIP8G is highly overall correlated with NPCIP8D and 1 other fieldsHigh correlation
NPCIP8K is highly overall correlated with NPCIP2B and 3 other fieldsHigh correlation
NPCIP2A is highly imbalanced (65.0%)Imbalance
NPCIP2C is highly imbalanced (58.5%)Imbalance
NPCIP2D is highly imbalanced (90.5%)Imbalance
NPCIP2E is highly imbalanced (74.7%)Imbalance
NPCIP2F is highly imbalanced (94.3%)Imbalance
NPCIP2G is highly imbalanced (98.5%)Imbalance
NPCIP6C is highly imbalanced (63.0%)Imbalance
NPCIP6D is highly imbalanced (60.6%)Imbalance
NPCIP6E is highly imbalanced (83.2%)Imbalance
NPCIP6G is highly imbalanced (84.6%)Imbalance
NPCIP6H is highly imbalanced (99.6%)Imbalance
NPCIP7A is highly imbalanced (80.0%)Imbalance
NPCIP7C is highly imbalanced (68.3%)Imbalance
NPCIP7D is highly imbalanced (80.3%)Imbalance
NPCIP7E is highly imbalanced (89.8%)Imbalance
NPCIP7F is highly imbalanced (56.7%)Imbalance
NPCIP7H is highly imbalanced (98.6%)Imbalance
NPCIP8J is highly imbalanced (96.1%)Imbalance
NPCIP12B is highly imbalanced (50.8%)Imbalance
NPCIP16B is highly imbalanced (77.4%)Imbalance
NPCIP16C is highly imbalanced (61.5%)Imbalance
NPCIP16E is highly imbalanced (66.1%)Imbalance
NPCIP16J is highly imbalanced (76.3%)Imbalance
NPCIP2A has 130798 (47.0%) missing valuesMissing
NPCIP2B has 130798 (47.0%) missing valuesMissing
NPCIP2C has 130798 (47.0%) missing valuesMissing
NPCIP2D has 130798 (47.0%) missing valuesMissing
NPCIP2E has 130798 (47.0%) missing valuesMissing
NPCIP2F has 130798 (47.0%) missing valuesMissing
NPCIP2G has 130798 (47.0%) missing valuesMissing
NPCIP2DA has 276226 (99.4%) missing valuesMissing
NPCIP3 has 130798 (47.0%) missing valuesMissing
NPCIP5 has 234032 (84.2%) missing valuesMissing
NPCIP6A has 43990 (15.8%) missing valuesMissing
NPCIP6B has 43990 (15.8%) missing valuesMissing
NPCIP6C has 43990 (15.8%) missing valuesMissing
NPCIP6D has 43990 (15.8%) missing valuesMissing
NPCIP6E has 43990 (15.8%) missing valuesMissing
NPCIP6F has 43990 (15.8%) missing valuesMissing
NPCIP6G has 43990 (15.8%) missing valuesMissing
NPCIP6H has 43990 (15.8%) missing valuesMissing
NPCIP7A has 43990 (15.8%) missing valuesMissing
NPCIP7B has 43990 (15.8%) missing valuesMissing
NPCIP7C has 43990 (15.8%) missing valuesMissing
NPCIP7D has 43990 (15.8%) missing valuesMissing
NPCIP7E has 43990 (15.8%) missing valuesMissing
NPCIP7F has 43990 (15.8%) missing valuesMissing
NPCIP7G has 43990 (15.8%) missing valuesMissing
NPCIP7H has 43990 (15.8%) missing valuesMissing
NPCIP8A has 43990 (15.8%) missing valuesMissing
NPCIP8B has 43990 (15.8%) missing valuesMissing
NPCIP8C has 43990 (15.8%) missing valuesMissing
NPCIP8D has 43990 (15.8%) missing valuesMissing
NPCIP8E has 43990 (15.8%) missing valuesMissing
NPCIP8F has 43990 (15.8%) missing valuesMissing
NPCIP8G has 43990 (15.8%) missing valuesMissing
NPCIP8H has 43990 (15.8%) missing valuesMissing
NPCIP8I has 43990 (15.8%) missing valuesMissing
NPCIP8K has 43990 (15.8%) missing valuesMissing
NPCIP8J has 43990 (15.8%) missing valuesMissing
NPCIP8DE has 141844 (51.0%) missing valuesMissing
NPCIP11A has 146134 (52.6%) missing valuesMissing
NPCIP11B has 160285 (57.7%) missing valuesMissing
NPCIP11C has 266413 (95.8%) missing valuesMissing
NPCIP11D has 257714 (92.7%) missing valuesMissing
NPCIP12A has 38417 (13.8%) missing valuesMissing
NPCIP12B has 38417 (13.8%) missing valuesMissing
NPCIP13A1 has 133340 (48.0%) missing valuesMissing
NPCIP13B1 has 182695 (65.7%) missing valuesMissing
NPCIP13A has 133340 (48.0%) missing valuesMissing
NPCIP13B has 182695 (65.7%) missing valuesMissing
NPCIP14 has 239605 (86.2%) missing valuesMissing
NPCIP14A has 277374 (99.8%) missing valuesMissing
NPCIP14B has 257715 (92.7%) missing valuesMissing
NPCIP16A has 17546 (6.3%) missing valuesMissing
NPCIP16B has 17546 (6.3%) missing valuesMissing
NPCIP16C has 17546 (6.3%) missing valuesMissing
NPCIP16D has 17546 (6.3%) missing valuesMissing
NPCIP16E has 17546 (6.3%) missing valuesMissing
NPCIP16F has 17546 (6.3%) missing valuesMissing
NPCIP16G has 17546 (6.3%) missing valuesMissing
NPCIP16H has 17546 (6.3%) missing valuesMissing
NPCIP16I has 17546 (6.3%) missing valuesMissing
NPCIP16J has 17546 (6.3%) missing valuesMissing
NPCIP17A has 102159 (36.7%) missing valuesMissing
NPCIP17B has 265721 (95.6%) missing valuesMissing
NPCIP17C has 90734 (32.6%) missing valuesMissing
NPCIP17D has 143426 (51.6%) missing valuesMissing
NPCIP17E has 125719 (45.2%) missing valuesMissing
NPCIP17F has 187265 (67.4%) missing valuesMissing
NPCIP17G has 186695 (67.2%) missing valuesMissing
NPCIP17H has 251569 (90.5%) missing valuesMissing
NPCIP17I has 140389 (50.5%) missing valuesMissing
NPCIP17J has 209199 (75.2%) missing valuesMissing
NPCIP17K has 244715 (88.0%) missing valuesMissing
NPCIP17L has 272519 (98.0%) missing valuesMissing
NPCIP17M has 276282 (99.4%) missing valuesMissing
SECUENCIA_P is highly skewed (γ1 = 20.16377013)Skewed
DIRECTORIO_PER has unique valuesUnique
NPCIP13A has 33945 (12.2%) zerosZeros
NPCIP13B has 2912 (1.0%) zerosZeros

Reproduction

Analysis started2024-04-22 02:09:20.229150
Analysis finished2024-04-22 02:10:49.541236
Duration1 minute and 29.31 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

DIRECTORIO
Real number (ℝ)

HIGH CORRELATION 

Distinct106467
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114302.2
Minimum166238
Maximum3006812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:49.640887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum166238
5-th percentile330593
Q1997796
median1052085.5
Q31150889
95-th percentile3000820
Maximum3006812
Range2840574
Interquartile range (IQR)153093

Descriptive statistics

Standard deviation604158.57
Coefficient of variation (CV)0.54218558
Kurtosis3.2390371
Mean1114302.2
Median Absolute Deviation (MAD)85603.5
Skewness1.56886
Sum3.0980052 × 1011
Variance3.6500757 × 1011
MonotonicityIncreasing
2024-04-21T21:10:49.783252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990174 21
 
< 0.1%
1166213 20
 
< 0.1%
342432 16
 
< 0.1%
326730 14
 
< 0.1%
1165204 14
 
< 0.1%
1024026 13
 
< 0.1%
987009 13
 
< 0.1%
327474 13
 
< 0.1%
2192412 12
 
< 0.1%
1016627 12
 
< 0.1%
Other values (106457) 277874
99.9%
ValueCountFrequency (%)
166238 2
 
< 0.1%
220102 4
< 0.1%
220385 3
< 0.1%
222175 5
< 0.1%
227359 2
 
< 0.1%
227362 2
 
< 0.1%
229477 4
< 0.1%
229508 4
< 0.1%
229753 2
 
< 0.1%
233197 4
< 0.1%
ValueCountFrequency (%)
3006812 3
< 0.1%
3006811 3
< 0.1%
3006810 6
< 0.1%
3006809 1
 
< 0.1%
3006808 2
 
< 0.1%
3006807 2
 
< 0.1%
3006806 3
< 0.1%
3006805 2
 
< 0.1%
3006804 2
 
< 0.1%
3006803 3
< 0.1%

DIRECTORIO_HOG
Real number (ℝ)

HIGH CORRELATION 

Distinct107119
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11143023
Minimum1662381
Maximum30068121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:49.920203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1662381
5-th percentile3305931
Q19977961
median10520856
Q311508891
95-th percentile30008201
Maximum30068121
Range28405740
Interquartile range (IQR)1530930

Descriptive statistics

Standard deviation6041585.7
Coefficient of variation (CV)0.54218553
Kurtosis3.2390371
Mean11143023
Median Absolute Deviation (MAD)856035
Skewness1.56886
Sum3.0980055 × 1012
Variance3.6500757 × 1013
MonotonicityIncreasing
2024-04-21T21:10:50.052440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11652041 14
 
< 0.1%
3267301 14
 
< 0.1%
10240261 13
 
< 0.1%
9981471 12
 
< 0.1%
11385171 11
 
< 0.1%
21931501 11
 
< 0.1%
11533751 11
 
< 0.1%
9966931 11
 
< 0.1%
10159131 11
 
< 0.1%
10023761 11
 
< 0.1%
Other values (107109) 277903
> 99.9%
ValueCountFrequency (%)
1662381 2
 
< 0.1%
2201021 4
< 0.1%
2203851 3
< 0.1%
2221751 5
< 0.1%
2273591 2
 
< 0.1%
2273621 2
 
< 0.1%
2294771 4
< 0.1%
2295081 4
< 0.1%
2297531 2
 
< 0.1%
2331971 4
< 0.1%
ValueCountFrequency (%)
30068121 3
< 0.1%
30068111 3
< 0.1%
30068101 6
< 0.1%
30068091 1
 
< 0.1%
30068081 2
 
< 0.1%
30068071 2
 
< 0.1%
30068061 3
< 0.1%
30068051 2
 
< 0.1%
30068041 2
 
< 0.1%
30068031 3
< 0.1%

DIRECTORIO_PER
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct278022
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1169805 × 108
Minimum16623811
Maximum2.1931501 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:50.197664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum16623811
5-th percentile33060313
Q199780936
median1.0521021 × 108
Q31.1509531 × 108
95-th percentile3.0008492 × 108
Maximum2.1931501 × 109
Range2.1765263 × 109
Interquartile range (IQR)15314377

Descriptive statistics

Standard deviation62658656
Coefficient of variation (CV)0.56096464
Kurtosis34.203392
Mean1.1169805 × 108
Median Absolute Deviation (MAD)8560349.5
Skewness2.7852828
Sum3.1054515 × 1013
Variance3.9261072 × 1015
MonotonicityNot monotonic
2024-04-21T21:10:50.341737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16623811 1
 
< 0.1%
112834611 1
 
< 0.1%
112833612 1
 
< 0.1%
112833711 1
 
< 0.1%
112833712 1
 
< 0.1%
112833713 1
 
< 0.1%
112833811 1
 
< 0.1%
112833911 1
 
< 0.1%
112833912 1
 
< 0.1%
112834011 1
 
< 0.1%
Other values (278012) 278012
> 99.9%
ValueCountFrequency (%)
16623811 1
< 0.1%
16623812 1
< 0.1%
22010211 1
< 0.1%
22010212 1
< 0.1%
22010213 1
< 0.1%
22010214 1
< 0.1%
22038511 1
< 0.1%
22038512 1
< 0.1%
22038513 1
< 0.1%
22217511 1
< 0.1%
ValueCountFrequency (%)
2193150111 1
< 0.1%
2193150110 1
< 0.1%
2187710110 1
< 0.1%
2168060110 1
< 0.1%
1930487110 1
< 0.1%
1165204114 1
< 0.1%
1165204113 1
< 0.1%
1165204112 1
< 0.1%
1165204111 1
< 0.1%
1165204110 1
< 0.1%

SECUENCIA_P
Real number (ℝ)

SKEWED 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.006352
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:50.455178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097423333
Coefficient of variation (CV)0.096808405
Kurtosis532.58317
Mean1.006352
Median Absolute Deviation (MAD)0
Skewness20.16377
Sum279788
Variance0.0094913058
MonotonicityNot monotonic
2024-04-21T21:10:50.555896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 276588
99.5%
2 1189
 
0.4%
3 178
 
0.1%
4 50
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
1 276588
99.5%
2 1189
 
0.4%
3 178
 
0.1%
4 50
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 14
 
< 0.1%
4 50
 
< 0.1%
3 178
 
0.1%
2 1189
 
0.4%
1 276588
99.5%

ORDEN
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1178971
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:50.650472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.206447
Coefficient of variation (CV)0.56964382
Kurtosis2.4031239
Mean2.1178971
Median Absolute Deviation (MAD)1
Skewness1.2880751
Sum588822
Variance1.4555144
MonotonicityNot monotonic
2024-04-21T21:10:50.763430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 107119
38.5%
2 84603
30.4%
3 50399
18.1%
4 24143
 
8.7%
5 7942
 
2.9%
6 2515
 
0.9%
7 819
 
0.3%
8 291
 
0.1%
9 103
 
< 0.1%
10 52
 
< 0.1%
Other values (4) 36
 
< 0.1%
ValueCountFrequency (%)
1 107119
38.5%
2 84603
30.4%
3 50399
18.1%
4 24143
 
8.7%
5 7942
 
2.9%
6 2515
 
0.9%
7 819
 
0.3%
8 291
 
0.1%
9 103
 
< 0.1%
10 52
 
< 0.1%
ValueCountFrequency (%)
14 2
 
< 0.1%
13 5
 
< 0.1%
12 8
 
< 0.1%
11 21
 
< 0.1%
10 52
 
< 0.1%
9 103
 
< 0.1%
8 291
 
0.1%
7 819
 
0.3%
6 2515
 
0.9%
5 7942
2.9%

NPCIP1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
5
130798 
1
112200 
2
27336 
3
 
6015
4
 
1673

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters278022
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

Length

2024-04-21T21:10:50.885961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:50.997827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

Most occurring characters

ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 278022
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 278022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 130798
47.0%
1 112200
40.4%
2 27336
 
9.8%
3 6015
 
2.2%
4 1673
 
0.6%

NPCIP2A
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
1.0
137547 
2.0
 
9677

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 137547
49.5%
2.0 9677
 
3.5%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:51.116117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:51.208224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 137547
93.4%
2.0 9677
 
6.6%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 137547
31.1%
2 9677
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
1 137547
46.7%
2 9677
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 137547
31.1%
2 9677
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 137547
31.1%
2 9677
 
2.2%

NPCIP2B
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
104297 
1.0
42927 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 104297
37.5%
1.0 42927
 
15.4%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:51.311940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:51.407309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 104297
70.8%
1.0 42927
29.2%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 104297
23.6%
1 42927
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 104297
35.4%
1 42927
 
14.6%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 104297
23.6%
1 42927
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 104297
23.6%
1 42927
 
9.7%

NPCIP2C
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
134889 
1.0
 
12335

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 134889
48.5%
1.0 12335
 
4.4%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:51.512574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:51.613515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 134889
91.6%
1.0 12335
 
8.4%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 134889
30.5%
1 12335
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 134889
45.8%
1 12335
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 134889
30.5%
1 12335
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 134889
30.5%
1 12335
 
2.8%

NPCIP2D
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
145428 
1.0
 
1796

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 145428
52.3%
1.0 1796
 
0.6%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:51.720545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:51.816275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 145428
98.8%
1.0 1796
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 145428
32.9%
1 1796
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 145428
49.4%
1 1796
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 145428
32.9%
1 1796
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 145428
32.9%
1 1796
 
0.4%

NPCIP2E
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
140981 
1.0
 
6243

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 140981
50.7%
1.0 6243
 
2.2%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:51.917215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:52.011654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 140981
95.8%
1.0 6243
 
4.2%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 140981
31.9%
1 6243
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 140981
47.9%
1 6243
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 140981
31.9%
1 6243
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 140981
31.9%
1 6243
 
1.4%

NPCIP2F
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
146260 
1.0
 
964

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 146260
52.6%
1.0 964
 
0.3%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:52.113893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:52.210611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 146260
99.3%
1.0 964
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 146260
33.1%
1 964
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 146260
49.7%
1 964
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 146260
33.1%
1 964
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 146260
33.1%
1 964
 
0.2%

NPCIP2G
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
2.0
147018 
1.0
 
206

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 147018
52.9%
1.0 206
 
0.1%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:52.317637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:52.414304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 147018
99.9%
1.0 206
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 147018
33.3%
1 206
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
2 147018
49.9%
1 206
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 147018
33.3%
1 206
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
2 147018
33.3%
1 206
 
< 0.1%

NPCIP2DA
Real number (ℝ)

MISSING 

Distinct62
Distinct (%)3.5%
Missing276226
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean9385.1609
Minimum0
Maximum100000
Zeros252
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:52.531508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12000
median5000
Q310000
95-th percentile30000
Maximum100000
Range100000
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation14211.575
Coefficient of variation (CV)1.5142601
Kurtosis16.841603
Mean9385.1609
Median Absolute Deviation (MAD)4000
Skewness3.6503405
Sum16855749
Variance2.0196885 × 108
MonotonicityNot monotonic
2024-04-21T21:10:52.943391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 348
 
0.1%
0 252
 
0.1%
2000 216
 
0.1%
10000 213
 
0.1%
3000 120
 
< 0.1%
20000 110
 
< 0.1%
15000 87
 
< 0.1%
1000 50
 
< 0.1%
6000 47
 
< 0.1%
30000 45
 
< 0.1%
Other values (52) 308
 
0.1%
(Missing) 276226
99.4%
ValueCountFrequency (%)
0 252
0.1%
500 15
 
< 0.1%
600 1
 
< 0.1%
800 5
 
< 0.1%
999 1
 
< 0.1%
1000 50
 
< 0.1%
1200 7
 
< 0.1%
1400 1
 
< 0.1%
1500 12
 
< 0.1%
1800 2
 
< 0.1%
ValueCountFrequency (%)
100000 15
< 0.1%
99000 1
 
< 0.1%
87000 1
 
< 0.1%
80000 4
 
< 0.1%
70000 3
 
< 0.1%
65000 1
 
< 0.1%
60000 13
< 0.1%
55000 1
 
< 0.1%
50000 22
< 0.1%
48000 1
 
< 0.1%

NPCIP3
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing130798
Missing (%)47.0%
Memory size2.1 MiB
1.0
64568 
2.0
47859 
3.0
30725 
4.0
 
4072

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441672
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 64568
23.2%
2.0 47859
 
17.2%
3.0 30725
 
11.1%
4.0 4072
 
1.5%
(Missing) 130798
47.0%

Length

2024-04-21T21:10:53.077043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:53.186601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64568
43.9%
2.0 47859
32.5%
3.0 30725
20.9%
4.0 4072
 
2.8%

Most occurring characters

ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 64568
14.6%
2 47859
 
10.8%
3 30725
 
7.0%
4 4072
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294448
66.7%
Other Punctuation 147224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147224
50.0%
1 64568
21.9%
2 47859
 
16.3%
3 30725
 
10.4%
4 4072
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 147224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 64568
14.6%
2 47859
 
10.8%
3 30725
 
7.0%
4 4072
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 147224
33.3%
0 147224
33.3%
1 64568
14.6%
2 47859
 
10.8%
3 30725
 
7.0%
4 4072
 
0.9%

NPCIP4
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
199683 
5
43990 
2
29966 
3
 
3519
4
 
864

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters278022
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

Length

2024-04-21T21:10:53.311380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:53.416436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 278022
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 278022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 199683
71.8%
5 43990
 
15.8%
2 29966
 
10.8%
3 3519
 
1.3%
4 864
 
0.3%

NPCIP5
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing234032
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean2.1499659
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:10:53.513621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5227904
Coefficient of variation (CV)0.70828582
Kurtosis1.8392643
Mean2.1499659
Median Absolute Deviation (MAD)1
Skewness1.5230819
Sum94577
Variance2.3188905
MonotonicityNot monotonic
2024-04-21T21:10:53.622168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 21448
 
7.7%
3 8467
 
3.0%
2 8289
 
3.0%
5 2120
 
0.8%
4 1393
 
0.5%
7 1340
 
0.5%
6 933
 
0.3%
(Missing) 234032
84.2%
ValueCountFrequency (%)
1 21448
7.7%
2 8289
 
3.0%
3 8467
 
3.0%
4 1393
 
0.5%
5 2120
 
0.8%
6 933
 
0.3%
7 1340
 
0.5%
ValueCountFrequency (%)
7 1340
 
0.5%
6 933
 
0.3%
5 2120
 
0.8%
4 1393
 
0.5%
3 8467
 
3.0%
2 8289
 
3.0%
1 21448
7.7%

NPCIP6A
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
161366 
1.0
72666 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 161366
58.0%
1.0 72666
26.1%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:53.732502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:53.825741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 161366
69.0%
1.0 72666
31.0%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 161366
23.0%
1 72666
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 161366
34.5%
1 72666
 
15.5%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 161366
23.0%
1 72666
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 161366
23.0%
1 72666
 
10.3%

NPCIP6B
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
153914 
1.0
80118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 153914
55.4%
1.0 80118
28.8%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:53.930496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.017723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 153914
65.8%
1.0 80118
34.2%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 153914
21.9%
1 80118
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 153914
32.9%
1 80118
 
17.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 153914
21.9%
1 80118
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 153914
21.9%
1 80118
 
11.4%

NPCIP6C
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
217418 
1.0
 
16614

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 217418
78.2%
1.0 16614
 
6.0%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:54.113273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.195797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 217418
92.9%
1.0 16614
 
7.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 217418
31.0%
1 16614
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 217418
46.5%
1 16614
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 217418
31.0%
1 16614
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 217418
31.0%
1 16614
 
2.4%

NPCIP6D
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
215833 
2.0
 
18199

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 215833
77.6%
2.0 18199
 
6.5%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:54.292823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.382790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 215833
92.2%
2.0 18199
 
7.8%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 215833
30.7%
2 18199
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 215833
46.1%
2 18199
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 215833
30.7%
2 18199
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 215833
30.7%
2 18199
 
2.6%

NPCIP6E
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
228231 
1.0
 
5801

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 228231
82.1%
1.0 5801
 
2.1%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:54.472609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.553367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 228231
97.5%
1.0 5801
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228231
32.5%
1 5801
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 228231
48.8%
1 5801
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228231
32.5%
1 5801
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228231
32.5%
1 5801
 
0.8%

NPCIP6F
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
202324 
1.0
31708 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 202324
72.8%
1.0 31708
 
11.4%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:54.655425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.750694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 202324
86.5%
1.0 31708
 
13.5%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 202324
28.8%
1 31708
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 202324
43.2%
1 31708
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 202324
28.8%
1 31708
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 202324
28.8%
1 31708
 
4.5%

NPCIP6G
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
228835 
1.0
 
5197

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 228835
82.3%
1.0 5197
 
1.9%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:54.852698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:54.946156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 228835
97.8%
1.0 5197
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228835
32.6%
1 5197
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 228835
48.9%
1 5197
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228835
32.6%
1 5197
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 228835
32.6%
1 5197
 
0.7%

NPCIP6H
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
233952 
1.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 233952
84.1%
1.0 80
 
< 0.1%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.038992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:55.126398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 233952
> 99.9%
1.0 80
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233952
33.3%
1 80
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 233952
50.0%
1 80
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233952
33.3%
1 80
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233952
33.3%
1 80
 
< 0.1%

NPCIP7A
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
226766 
2.0
 
7266

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 226766
81.6%
2.0 7266
 
2.6%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.216701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:55.306858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 226766
96.9%
2.0 7266
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 226766
32.3%
2 7266
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 226766
48.4%
2 7266
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 226766
32.3%
2 7266
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 226766
32.3%
2 7266
 
1.0%

NPCIP7B
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
170514 
1.0
63518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 170514
61.3%
1.0 63518
 
22.8%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.400103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:55.486976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 170514
72.9%
1.0 63518
 
27.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 170514
24.3%
1 63518
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 170514
36.4%
1 63518
 
13.6%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 170514
24.3%
1 63518
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 170514
24.3%
1 63518
 
9.0%

NPCIP7C
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
220596 
1.0
 
13436

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 220596
79.3%
1.0 13436
 
4.8%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.580657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:55.660729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 220596
94.3%
1.0 13436
 
5.7%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 220596
31.4%
1 13436
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 220596
47.1%
1 13436
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 220596
31.4%
1 13436
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 220596
31.4%
1 13436
 
1.9%

NPCIP7D
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
226890 
1.0
 
7142

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 226890
81.6%
1.0 7142
 
2.6%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.747850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:55.827230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 226890
96.9%
1.0 7142
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 226890
32.3%
1 7142
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 226890
48.5%
1 7142
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 226890
32.3%
1 7142
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 226890
32.3%
1 7142
 
1.0%

NPCIP7E
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
230933 
1.0
 
3099

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 230933
83.1%
1.0 3099
 
1.1%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:55.922383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.008384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 230933
98.7%
1.0 3099
 
1.3%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 230933
32.9%
1 3099
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 230933
49.3%
1 3099
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 230933
32.9%
1 3099
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 230933
32.9%
1 3099
 
0.4%

NPCIP7F
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
213214 
1.0
 
20818

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 213214
76.7%
1.0 20818
 
7.5%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:56.096996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.180141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 213214
91.1%
1.0 20818
 
8.9%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 213214
30.4%
1 20818
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 213214
45.6%
1 20818
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 213214
30.4%
1 20818
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 213214
30.4%
1 20818
 
3.0%

NPCIP7G
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
195673 
1.0
38359 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 195673
70.4%
1.0 38359
 
13.8%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:56.271576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.354095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 195673
83.6%
1.0 38359
 
16.4%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 195673
27.9%
1 38359
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 195673
41.8%
1 38359
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 195673
27.9%
1 38359
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 195673
27.9%
1 38359
 
5.5%

NPCIP7H
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
233736 
1.0
 
296

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 233736
84.1%
1.0 296
 
0.1%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:56.445486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.545561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 233736
99.9%
1.0 296
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233736
33.3%
1 296
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 233736
49.9%
1 296
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233736
33.3%
1 296
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233736
33.3%
1 296
 
< 0.1%

NPCIP8A
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
170840 
2.0
63192 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 170840
61.4%
2.0 63192
 
22.7%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:56.651919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.744869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 170840
73.0%
2.0 63192
 
27.0%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 170840
24.3%
2 63192
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 170840
36.5%
2 63192
 
13.5%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 170840
24.3%
2 63192
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 170840
24.3%
2 63192
 
9.0%

NPCIP8B
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
184122 
2.0
49910 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 184122
66.2%
2.0 49910
 
18.0%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:56.845957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:56.935142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 184122
78.7%
2.0 49910
 
21.3%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 184122
26.2%
2 49910
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 184122
39.3%
2 49910
 
10.7%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 184122
26.2%
2 49910
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 184122
26.2%
2 49910
 
7.1%

NPCIP8C
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
186984 
2.0
47048 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 186984
67.3%
2.0 47048
 
16.9%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.032436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:57.123324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 186984
79.9%
2.0 47048
 
20.1%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 186984
26.6%
2 47048
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 186984
39.9%
2 47048
 
10.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 186984
26.6%
2 47048
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 186984
26.6%
2 47048
 
6.7%

NPCIP8D
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
157823 
1.0
76209 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 157823
56.8%
1.0 76209
27.4%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.226024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:57.318181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 157823
67.4%
1.0 76209
32.6%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 157823
22.5%
1 76209
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 157823
33.7%
1 76209
 
16.3%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 157823
22.5%
1 76209
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 157823
22.5%
1 76209
 
10.9%

NPCIP8E
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
148885 
1.0
85147 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 148885
53.6%
1.0 85147
30.6%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.418275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:57.504679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 148885
63.6%
1.0 85147
36.4%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 148885
21.2%
1 85147
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 148885
31.8%
1 85147
 
18.2%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 148885
21.2%
1 85147
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 148885
21.2%
1 85147
 
12.1%

NPCIP8F
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
121879 
1.0
112153 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 121879
43.8%
1.0 112153
40.3%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.598999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:57.686629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 121879
52.1%
1.0 112153
47.9%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 121879
17.4%
1 112153
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 121879
26.0%
1 112153
24.0%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 121879
17.4%
1 112153
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 121879
17.4%
1 112153
16.0%

NPCIP8G
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
185936 
1.0
48096 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 185936
66.9%
1.0 48096
 
17.3%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.780578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:57.874959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 185936
79.4%
1.0 48096
 
20.6%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 185936
26.5%
1 48096
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 185936
39.7%
1 48096
 
10.3%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 185936
26.5%
1 48096
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 185936
26.5%
1 48096
 
6.9%

NPCIP8H
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
1.0
119559 
2.0
114473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 119559
43.0%
2.0 114473
41.2%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:57.969919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:58.060221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 119559
51.1%
2.0 114473
48.9%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 119559
17.0%
2 114473
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
1 119559
25.5%
2 114473
24.5%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 119559
17.0%
2 114473
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
1 119559
17.0%
2 114473
16.3%

NPCIP8I
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
149273 
1.0
84759 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 149273
53.7%
1.0 84759
30.5%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:58.169903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:58.262932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 149273
63.8%
1.0 84759
36.2%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 149273
21.3%
1 84759
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 149273
31.9%
1 84759
 
18.1%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 149273
21.3%
1 84759
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 149273
21.3%
1 84759
 
12.1%

NPCIP8K
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
159136 
1.0
74896 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 159136
57.2%
1.0 74896
26.9%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:58.366458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:58.454240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 159136
68.0%
1.0 74896
32.0%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 159136
22.7%
1 74896
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 159136
34.0%
1 74896
 
16.0%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 159136
22.7%
1 74896
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 159136
22.7%
1 74896
 
10.7%

NPCIP8J
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing43990
Missing (%)15.8%
Memory size2.1 MiB
2.0
233059 
1.0
 
973

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702096
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 233059
83.8%
1.0 973
 
0.3%
(Missing) 43990
 
15.8%

Length

2024-04-21T21:10:58.550855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:58.641045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 233059
99.6%
1.0 973
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233059
33.2%
1 973
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 468064
66.7%
Other Punctuation 234032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234032
50.0%
2 233059
49.8%
1 973
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 234032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 702096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233059
33.2%
1 973
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 702096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 234032
33.3%
0 234032
33.3%
2 233059
33.2%
1 973
 
0.1%

NPCIP8DE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing141844
Missing (%)51.0%
Memory size2.1 MiB
3.0
62349 
1.0
41839 
2.0
21567 
4.0
10423 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters408534
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 62349
22.4%
1.0 41839
 
15.0%
2.0 21567
 
7.8%
4.0 10423
 
3.7%
(Missing) 141844
51.0%

Length

2024-04-21T21:10:58.740363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:58.851890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 62349
45.8%
1.0 41839
30.7%
2.0 21567
 
15.8%
4.0 10423
 
7.7%

Most occurring characters

ValueCountFrequency (%)
. 136178
33.3%
0 136178
33.3%
3 62349
15.3%
1 41839
 
10.2%
2 21567
 
5.3%
4 10423
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272356
66.7%
Other Punctuation 136178
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136178
50.0%
3 62349
22.9%
1 41839
 
15.4%
2 21567
 
7.9%
4 10423
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 136178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 408534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 136178
33.3%
0 136178
33.3%
3 62349
15.3%
1 41839
 
10.2%
2 21567
 
5.3%
4 10423
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 136178
33.3%
0 136178
33.3%
3 62349
15.3%
1 41839
 
10.2%
2 21567
 
5.3%
4 10423
 
2.6%

NPCIP11A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing146134
Missing (%)52.6%
Memory size2.1 MiB
1.0
131888 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters395664
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 131888
47.4%
(Missing) 146134
52.6%

Length

2024-04-21T21:10:58.966885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:59.065786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 131888
100.0%

Most occurring characters

ValueCountFrequency (%)
1 131888
33.3%
. 131888
33.3%
0 131888
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263776
66.7%
Other Punctuation 131888
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 131888
50.0%
0 131888
50.0%
Other Punctuation
ValueCountFrequency (%)
. 131888
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 395664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 131888
33.3%
. 131888
33.3%
0 131888
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 131888
33.3%
. 131888
33.3%
0 131888
33.3%

NPCIP11B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing160285
Missing (%)57.7%
Memory size2.1 MiB
1.0
117737 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters353211
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 117737
42.3%
(Missing) 160285
57.7%

Length

2024-04-21T21:10:59.165500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:59.259994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 117737
100.0%

Most occurring characters

ValueCountFrequency (%)
1 117737
33.3%
. 117737
33.3%
0 117737
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235474
66.7%
Other Punctuation 117737
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 117737
50.0%
0 117737
50.0%
Other Punctuation
ValueCountFrequency (%)
. 117737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 353211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 117737
33.3%
. 117737
33.3%
0 117737
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 117737
33.3%
. 117737
33.3%
0 117737
33.3%

NPCIP11C
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing266413
Missing (%)95.8%
Memory size2.1 MiB
1.0
11609 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34827
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11609
 
4.2%
(Missing) 266413
95.8%

Length

2024-04-21T21:10:59.357500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:59.450770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11609
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11609
33.3%
. 11609
33.3%
0 11609
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23218
66.7%
Other Punctuation 11609
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11609
50.0%
0 11609
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34827
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11609
33.3%
. 11609
33.3%
0 11609
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11609
33.3%
. 11609
33.3%
0 11609
33.3%

NPCIP11D
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257714
Missing (%)92.7%
Memory size2.1 MiB
1.0
20308 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60924
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 20308
 
7.3%
(Missing) 257714
92.7%

Length

2024-04-21T21:10:59.543628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:59.637417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20308
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20308
33.3%
. 20308
33.3%
0 20308
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40616
66.7%
Other Punctuation 20308
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20308
50.0%
0 20308
50.0%
Other Punctuation
ValueCountFrequency (%)
. 20308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20308
33.3%
. 20308
33.3%
0 20308
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20308
33.3%
. 20308
33.3%
0 20308
33.3%

NPCIP12
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
239605 
2
38417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters278022
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

Length

2024-04-21T21:10:59.743085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:10:59.831558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

Most occurring characters

ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 278022
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 278022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 239605
86.2%
2 38417
 
13.8%

NPCIP12A
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing38417
Missing (%)13.8%
Memory size2.1 MiB
2.0
202390 
1.0
37215 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters718815
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 202390
72.8%
1.0 37215
 
13.4%
(Missing) 38417
 
13.8%

Length

2024-04-21T21:10:59.937066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:00.043973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 202390
84.5%
1.0 37215
 
15.5%

Most occurring characters

ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
2 202390
28.2%
1 37215
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 479210
66.7%
Other Punctuation 239605
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 239605
50.0%
2 202390
42.2%
1 37215
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 239605
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 718815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
2 202390
28.2%
1 37215
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 718815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
2 202390
28.2%
1 37215
 
5.2%

NPCIP12B
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing38417
Missing (%)13.8%
Memory size2.1 MiB
1.0
213841 
2.0
25764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters718815
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 213841
76.9%
2.0 25764
 
9.3%
(Missing) 38417
 
13.8%

Length

2024-04-21T21:11:00.143350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:00.231991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 213841
89.2%
2.0 25764
 
10.8%

Most occurring characters

ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
1 213841
29.7%
2 25764
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 479210
66.7%
Other Punctuation 239605
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 239605
50.0%
1 213841
44.6%
2 25764
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 239605
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 718815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
1 213841
29.7%
2 25764
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 718815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 239605
33.3%
0 239605
33.3%
1 213841
29.7%
2 25764
 
3.6%

NPCIP13A1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing133340
Missing (%)48.0%
Memory size2.1 MiB
1.0
144682 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters434046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 144682
52.0%
(Missing) 133340
48.0%

Length

2024-04-21T21:11:00.335283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:00.430720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 144682
100.0%

Most occurring characters

ValueCountFrequency (%)
1 144682
33.3%
. 144682
33.3%
0 144682
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 289364
66.7%
Other Punctuation 144682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 144682
50.0%
0 144682
50.0%
Other Punctuation
ValueCountFrequency (%)
. 144682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 434046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 144682
33.3%
. 144682
33.3%
0 144682
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 434046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 144682
33.3%
. 144682
33.3%
0 144682
33.3%

NPCIP13B1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing182695
Missing (%)65.7%
Memory size2.1 MiB
1.0
95327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 95327
34.3%
(Missing) 182695
65.7%

Length

2024-04-21T21:11:00.531135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:00.618841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 95327
100.0%

Most occurring characters

ValueCountFrequency (%)
1 95327
33.3%
. 95327
33.3%
0 95327
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190654
66.7%
Other Punctuation 95327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 95327
50.0%
0 95327
50.0%
Other Punctuation
ValueCountFrequency (%)
. 95327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 285981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 95327
33.3%
. 95327
33.3%
0 95327
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 285981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 95327
33.3%
. 95327
33.3%
0 95327
33.3%

NPCIP13A
Real number (ℝ)

MISSING  ZEROS 

Distinct220
Distinct (%)0.2%
Missing133340
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean12037.89
Minimum0
Maximum100000
Zeros33945
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:11:00.732477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median10000
Q320000
95-th percentile35000
Maximum100000
Range100000
Interquartile range (IQR)19000

Descriptive statistics

Standard deviation13309.043
Coefficient of variation (CV)1.105596
Kurtosis6.6154024
Mean12037.89
Median Absolute Deviation (MAD)10000
Skewness1.9997566
Sum1.741666 × 109
Variance1.7713063 × 108
MonotonicityNot monotonic
2024-04-21T21:11:01.228344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33945
 
12.2%
10000 23630
 
8.5%
20000 17209
 
6.2%
5000 13625
 
4.9%
30000 6975
 
2.5%
15000 6796
 
2.4%
6000 6246
 
2.2%
2000 5695
 
2.0%
24000 4506
 
1.6%
25000 3960
 
1.4%
Other values (210) 22095
 
7.9%
(Missing) 133340
48.0%
ValueCountFrequency (%)
0 33945
12.2%
1000 3699
 
1.3%
1008 4
 
< 0.1%
1080 1
 
< 0.1%
1200 3
 
< 0.1%
1500 45
 
< 0.1%
1555 1
 
< 0.1%
1800 1
 
< 0.1%
1880 1
 
< 0.1%
2000 5695
 
2.0%
ValueCountFrequency (%)
100000 274
0.1%
99999 2
 
< 0.1%
99000 2
 
< 0.1%
98000 6
 
< 0.1%
96000 3
 
< 0.1%
95000 10
 
< 0.1%
94000 1
 
< 0.1%
92000 1
 
< 0.1%
91000 1
 
< 0.1%
90000 42
 
< 0.1%

NPCIP13B
Real number (ℝ)

MISSING  ZEROS 

Distinct761
Distinct (%)0.8%
Missing182695
Missing (%)65.7%
Infinite0
Infinite (%)0.0%
Mean54340.895
Minimum0
Maximum1000000
Zeros2912
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-21T21:11:01.370349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20000
Q135000
median50000
Q363000
95-th percentile100000
Maximum1000000
Range1000000
Interquartile range (IQR)28000

Descriptive statistics

Standard deviation44729.946
Coefficient of variation (CV)0.82313597
Kurtosis160.59809
Mean54340.895
Median Absolute Deviation (MAD)15000
Skewness10.198655
Sum5.1801545 × 109
Variance2.000768 × 109
MonotonicityNot monotonic
2024-04-21T21:11:01.517157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 9572
 
3.4%
35000 8811
 
3.2%
60000 8542
 
3.1%
45000 6537
 
2.4%
40000 5918
 
2.1%
30000 5152
 
1.9%
70000 3948
 
1.4%
55000 3427
 
1.2%
65000 3012
 
1.1%
80000 2986
 
1.1%
Other values (751) 37422
 
13.5%
(Missing) 182695
65.7%
ValueCountFrequency (%)
0 2912
1.0%
5000 495
 
0.2%
5500 3
 
< 0.1%
5600 1
 
< 0.1%
5800 1
 
< 0.1%
6000 89
 
< 0.1%
6600 1
 
< 0.1%
7000 16
 
< 0.1%
7500 1
 
< 0.1%
8000 8
 
< 0.1%
ValueCountFrequency (%)
1000000 20
< 0.1%
999999 3
 
< 0.1%
988888 1
 
< 0.1%
980000 2
 
< 0.1%
968500 1
 
< 0.1%
960000 1
 
< 0.1%
950000 1
 
< 0.1%
930000 1
 
< 0.1%
924000 1
 
< 0.1%
920000 1
 
< 0.1%

NPCIP14
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing239605
Missing (%)86.2%
Memory size2.1 MiB
1.0
20626 
2.0
17791 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters115251
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 20626
 
7.4%
2.0 17791
 
6.4%
(Missing) 239605
86.2%

Length

2024-04-21T21:11:01.647119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:01.741931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20626
53.7%
2.0 17791
46.3%

Most occurring characters

ValueCountFrequency (%)
. 38417
33.3%
0 38417
33.3%
1 20626
17.9%
2 17791
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76834
66.7%
Other Punctuation 38417
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38417
50.0%
1 20626
26.8%
2 17791
23.2%
Other Punctuation
ValueCountFrequency (%)
. 38417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 38417
33.3%
0 38417
33.3%
1 20626
17.9%
2 17791
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 38417
33.3%
0 38417
33.3%
1 20626
17.9%
2 17791
15.4%

NPCIP14A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing277374
Missing (%)99.8%
Memory size2.1 MiB
1.0
648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1944
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 648
 
0.2%
(Missing) 277374
99.8%

Length

2024-04-21T21:11:01.847626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:01.942908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 648
100.0%

Most occurring characters

ValueCountFrequency (%)
1 648
33.3%
. 648
33.3%
0 648
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296
66.7%
Other Punctuation 648
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 648
50.0%
0 648
50.0%
Other Punctuation
ValueCountFrequency (%)
. 648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 648
33.3%
. 648
33.3%
0 648
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 648
33.3%
. 648
33.3%
0 648
33.3%

NPCIP14B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257715
Missing (%)92.7%
Memory size2.1 MiB
1.0
20307 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60921
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 20307
 
7.3%
(Missing) 257715
92.7%

Length

2024-04-21T21:11:02.041689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:02.138621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20307
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20307
33.3%
. 20307
33.3%
0 20307
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40614
66.7%
Other Punctuation 20307
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20307
50.0%
0 20307
50.0%
Other Punctuation
ValueCountFrequency (%)
. 20307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20307
33.3%
. 20307
33.3%
0 20307
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20307
33.3%
. 20307
33.3%
0 20307
33.3%

NPCIP16A
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
1.0
230348 
2.0
30128 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 230348
82.9%
2.0 30128
 
10.8%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:02.242854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:02.341001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 230348
88.4%
2.0 30128
 
11.6%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 230348
29.5%
2 30128
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
1 230348
44.2%
2 30128
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 230348
29.5%
2 30128
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 230348
29.5%
2 30128
 
3.9%

NPCIP16B
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
250983 
1.0
 
9493

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 250983
90.3%
1.0 9493
 
3.4%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:02.448396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:02.535832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 250983
96.4%
1.0 9493
 
3.6%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250983
32.1%
1 9493
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 250983
48.2%
1 9493
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250983
32.1%
1 9493
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250983
32.1%
1 9493
 
1.2%

NPCIP16C
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
1.0
240870 
2.0
 
19606

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 240870
86.6%
2.0 19606
 
7.1%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:02.628882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:02.729550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 240870
92.5%
2.0 19606
 
7.5%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 240870
30.8%
2 19606
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
1 240870
46.2%
2 19606
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 240870
30.8%
2 19606
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 240870
30.8%
2 19606
 
2.5%

NPCIP16D
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
1.0
209345 
2.0
51131 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 209345
75.3%
2.0 51131
 
18.4%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:02.829965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:02.924458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 209345
80.4%
2.0 51131
 
19.6%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 209345
26.8%
2 51131
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
1 209345
40.2%
2 51131
 
9.8%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 209345
26.8%
2 51131
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
1 209345
26.8%
2 51131
 
6.5%

NPCIP16E
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
244089 
1.0
 
16387

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 244089
87.8%
1.0 16387
 
5.9%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:03.028209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:03.128060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 244089
93.7%
1.0 16387
 
6.3%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 244089
31.2%
1 16387
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 244089
46.9%
1 16387
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 244089
31.2%
1 16387
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 244089
31.2%
1 16387
 
2.1%

NPCIP16F
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
134053 
1.0
126423 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 134053
48.2%
1.0 126423
45.5%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:03.224024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:03.313442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 134053
51.5%
1.0 126423
48.5%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134053
17.2%
1 126423
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 134053
25.7%
1 126423
24.3%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134053
17.2%
1 126423
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134053
17.2%
1 126423
16.2%

NPCIP16G
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
134605 
1.0
125871 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 134605
48.4%
1.0 125871
45.3%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:03.410869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:03.500054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 134605
51.7%
1.0 125871
48.3%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134605
17.2%
1 125871
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 134605
25.8%
1 125871
24.2%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134605
17.2%
1 125871
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 134605
17.2%
1 125871
16.1%

NPCIP16H
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
221617 
1.0
38859 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 221617
79.7%
1.0 38859
 
14.0%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:03.604546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:03.712556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 221617
85.1%
1.0 38859
 
14.9%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 221617
28.4%
1 38859
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 221617
42.5%
1 38859
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 221617
28.4%
1 38859
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 221617
28.4%
1 38859
 
5.0%

NPCIP16I
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
192754 
1.0
67722 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 192754
69.3%
1.0 67722
 
24.4%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:03.813307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:03.908929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 192754
74.0%
1.0 67722
 
26.0%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 192754
24.7%
1 67722
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 192754
37.0%
1 67722
 
13.0%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 192754
24.7%
1 67722
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 192754
24.7%
1 67722
 
8.7%

NPCIP16J
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17546
Missing (%)6.3%
Memory size2.1 MiB
2.0
250372 
1.0
 
10104

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters781428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 250372
90.1%
1.0 10104
 
3.6%
(Missing) 17546
 
6.3%

Length

2024-04-21T21:11:04.010756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:04.101138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 250372
96.1%
1.0 10104
 
3.9%

Most occurring characters

ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250372
32.0%
1 10104
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520952
66.7%
Other Punctuation 260476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260476
50.0%
2 250372
48.1%
1 10104
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 781428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250372
32.0%
1 10104
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 781428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 260476
33.3%
0 260476
33.3%
2 250372
32.0%
1 10104
 
1.3%

NPCIP17A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing102159
Missing (%)36.7%
Memory size2.1 MiB
1.0
175863 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters527589
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 175863
63.3%
(Missing) 102159
36.7%

Length

2024-04-21T21:11:04.201747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:04.303596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 175863
100.0%

Most occurring characters

ValueCountFrequency (%)
1 175863
33.3%
. 175863
33.3%
0 175863
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 351726
66.7%
Other Punctuation 175863
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 175863
50.0%
0 175863
50.0%
Other Punctuation
ValueCountFrequency (%)
. 175863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 527589
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 175863
33.3%
. 175863
33.3%
0 175863
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 527589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 175863
33.3%
. 175863
33.3%
0 175863
33.3%

NPCIP17B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing265721
Missing (%)95.6%
Memory size2.1 MiB
1.0
12301 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36903
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12301
 
4.4%
(Missing) 265721
95.6%

Length

2024-04-21T21:11:04.411374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:04.508558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12301
100.0%

Most occurring characters

ValueCountFrequency (%)
1 12301
33.3%
. 12301
33.3%
0 12301
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24602
66.7%
Other Punctuation 12301
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12301
50.0%
0 12301
50.0%
Other Punctuation
ValueCountFrequency (%)
. 12301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12301
33.3%
. 12301
33.3%
0 12301
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12301
33.3%
. 12301
33.3%
0 12301
33.3%

NPCIP17C
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing90734
Missing (%)32.6%
Memory size2.1 MiB
1.0
187288 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters561864
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 187288
67.4%
(Missing) 90734
32.6%

Length

2024-04-21T21:11:04.605917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:04.700319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 187288
100.0%

Most occurring characters

ValueCountFrequency (%)
1 187288
33.3%
. 187288
33.3%
0 187288
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 374576
66.7%
Other Punctuation 187288
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 187288
50.0%
0 187288
50.0%
Other Punctuation
ValueCountFrequency (%)
. 187288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 561864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 187288
33.3%
. 187288
33.3%
0 187288
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 561864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 187288
33.3%
. 187288
33.3%
0 187288
33.3%

NPCIP17D
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing143426
Missing (%)51.6%
Memory size2.1 MiB
1.0
134596 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters403788
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 134596
48.4%
(Missing) 143426
51.6%

Length

2024-04-21T21:11:04.800071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:04.891237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 134596
100.0%

Most occurring characters

ValueCountFrequency (%)
1 134596
33.3%
. 134596
33.3%
0 134596
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269192
66.7%
Other Punctuation 134596
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 134596
50.0%
0 134596
50.0%
Other Punctuation
ValueCountFrequency (%)
. 134596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 403788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 134596
33.3%
. 134596
33.3%
0 134596
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 403788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 134596
33.3%
. 134596
33.3%
0 134596
33.3%

NPCIP17E
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing125719
Missing (%)45.2%
Memory size2.1 MiB
1.0
152303 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters456909
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 152303
54.8%
(Missing) 125719
45.2%

Length

2024-04-21T21:11:04.984318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.075033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 152303
100.0%

Most occurring characters

ValueCountFrequency (%)
1 152303
33.3%
. 152303
33.3%
0 152303
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304606
66.7%
Other Punctuation 152303
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 152303
50.0%
0 152303
50.0%
Other Punctuation
ValueCountFrequency (%)
. 152303
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 456909
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 152303
33.3%
. 152303
33.3%
0 152303
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 152303
33.3%
. 152303
33.3%
0 152303
33.3%

NPCIP17F
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing187265
Missing (%)67.4%
Memory size2.1 MiB
1.0
90757 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272271
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 90757
32.6%
(Missing) 187265
67.4%

Length

2024-04-21T21:11:05.171917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.257802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 90757
100.0%

Most occurring characters

ValueCountFrequency (%)
1 90757
33.3%
. 90757
33.3%
0 90757
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181514
66.7%
Other Punctuation 90757
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 90757
50.0%
0 90757
50.0%
Other Punctuation
ValueCountFrequency (%)
. 90757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 90757
33.3%
. 90757
33.3%
0 90757
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 90757
33.3%
. 90757
33.3%
0 90757
33.3%

NPCIP17G
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing186695
Missing (%)67.2%
Memory size2.1 MiB
1.0
91327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters273981
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 91327
32.8%
(Missing) 186695
67.2%

Length

2024-04-21T21:11:05.346454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.433369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 91327
100.0%

Most occurring characters

ValueCountFrequency (%)
1 91327
33.3%
. 91327
33.3%
0 91327
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182654
66.7%
Other Punctuation 91327
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91327
50.0%
0 91327
50.0%
Other Punctuation
ValueCountFrequency (%)
. 91327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 273981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 91327
33.3%
. 91327
33.3%
0 91327
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 91327
33.3%
. 91327
33.3%
0 91327
33.3%

NPCIP17H
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing251569
Missing (%)90.5%
Memory size2.1 MiB
1.0
26453 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters79359
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 26453
 
9.5%
(Missing) 251569
90.5%

Length

2024-04-21T21:11:05.524340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.614385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 26453
100.0%

Most occurring characters

ValueCountFrequency (%)
1 26453
33.3%
. 26453
33.3%
0 26453
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52906
66.7%
Other Punctuation 26453
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26453
50.0%
0 26453
50.0%
Other Punctuation
ValueCountFrequency (%)
. 26453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26453
33.3%
. 26453
33.3%
0 26453
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26453
33.3%
. 26453
33.3%
0 26453
33.3%

NPCIP17I
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing140389
Missing (%)50.5%
Memory size2.1 MiB
1.0
137633 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters412899
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 137633
49.5%
(Missing) 140389
50.5%

Length

2024-04-21T21:11:05.713560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.806035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 137633
100.0%

Most occurring characters

ValueCountFrequency (%)
1 137633
33.3%
. 137633
33.3%
0 137633
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 275266
66.7%
Other Punctuation 137633
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 137633
50.0%
0 137633
50.0%
Other Punctuation
ValueCountFrequency (%)
. 137633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 412899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 137633
33.3%
. 137633
33.3%
0 137633
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 137633
33.3%
. 137633
33.3%
0 137633
33.3%

NPCIP17J
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing209199
Missing (%)75.2%
Memory size2.1 MiB
1.0
68823 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters206469
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 68823
 
24.8%
(Missing) 209199
75.2%

Length

2024-04-21T21:11:05.903198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:05.990156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 68823
100.0%

Most occurring characters

ValueCountFrequency (%)
1 68823
33.3%
. 68823
33.3%
0 68823
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137646
66.7%
Other Punctuation 68823
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 68823
50.0%
0 68823
50.0%
Other Punctuation
ValueCountFrequency (%)
. 68823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 206469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 68823
33.3%
. 68823
33.3%
0 68823
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 68823
33.3%
. 68823
33.3%
0 68823
33.3%

NPCIP17K
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing244715
Missing (%)88.0%
Memory size2.1 MiB
1.0
33307 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters99921
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33307
 
12.0%
(Missing) 244715
88.0%

Length

2024-04-21T21:11:06.080340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:06.166276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33307
100.0%

Most occurring characters

ValueCountFrequency (%)
1 33307
33.3%
. 33307
33.3%
0 33307
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66614
66.7%
Other Punctuation 33307
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33307
50.0%
0 33307
50.0%
Other Punctuation
ValueCountFrequency (%)
. 33307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33307
33.3%
. 33307
33.3%
0 33307
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33307
33.3%
. 33307
33.3%
0 33307
33.3%

NPCIP17L
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing272519
Missing (%)98.0%
Memory size2.1 MiB
1.0
5503 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16509
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5503
 
2.0%
(Missing) 272519
98.0%

Length

2024-04-21T21:11:06.257132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:06.341122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5503
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5503
33.3%
. 5503
33.3%
0 5503
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11006
66.7%
Other Punctuation 5503
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5503
50.0%
0 5503
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16509
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5503
33.3%
. 5503
33.3%
0 5503
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5503
33.3%
. 5503
33.3%
0 5503
33.3%

NPCIP17M
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing276282
Missing (%)99.4%
Memory size2.1 MiB
1.0
1740 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5220
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1740
 
0.6%
(Missing) 276282
99.4%

Length

2024-04-21T21:11:06.433604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:11:06.529948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1740
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1740
33.3%
. 1740
33.3%
0 1740
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3480
66.7%
Other Punctuation 1740
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1740
50.0%
0 1740
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1740
33.3%
. 1740
33.3%
0 1740
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1740
33.3%
. 1740
33.3%
0 1740
33.3%

FEX_C
Text

Distinct65203
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-04-21T21:11:06.775610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.887843
Min length11

Characters and Unicode

Total characters4417170
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5024 ?
Unique (%)1.8%

Sample

1st row1,69849246231156
2nd row1,69849246231156
3rd row6,74441212899351
4th row6,74441212899351
5th row6,74441212899351
ValueCountFrequency (%)
1,80463242698892 1865
 
0.7%
1,13167701863354 1486
 
0.5%
1,4395777439874 614
 
0.2%
1,69849246231156 564
 
0.2%
1,1996481199895 416
 
0.1%
10,7610389527659 250
 
0.1%
29,4959010692373 178
 
0.1%
1,18106995884774 165
 
0.1%
1,30870703998855 145
 
0.1%
19,7627135909443 130
 
< 0.1%
Other values (65193) 272209
97.9%
2024-04-21T21:11:07.192249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 463115
10.5%
2 447732
10.1%
3 430124
9.7%
4 426051
9.6%
5 408249
9.2%
6 407965
9.2%
8 402352
9.1%
7 399074
9.0%
9 394984
8.9%
0 359502
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4139148
93.7%
Other Punctuation 278022
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 463115
11.2%
2 447732
10.8%
3 430124
10.4%
4 426051
10.3%
5 408249
9.9%
6 407965
9.9%
8 402352
9.7%
7 399074
9.6%
9 394984
9.5%
0 359502
8.7%
Other Punctuation
ValueCountFrequency (%)
, 278022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4417170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 463115
10.5%
2 447732
10.1%
3 430124
9.7%
4 426051
9.6%
5 408249
9.2%
6 407965
9.2%
8 402352
9.1%
7 399074
9.0%
9 394984
8.9%
0 359502
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4417170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 463115
10.5%
2 447732
10.1%
3 430124
9.7%
4 426051
9.6%
5 408249
9.2%
6 407965
9.2%
8 402352
9.1%
7 399074
9.0%
9 394984
8.9%
0 359502
8.1%

Interactions

2024-04-21T21:10:38.668704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.366791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.392955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.437974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.647978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.665828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.677673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.576923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.492236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.800665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.486032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.508249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.557922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.769240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.793373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.778949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.678076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.602222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.918608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.601551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.632849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.679374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.886612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.916212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.882756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.779582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.718225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.031161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.719028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.747759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.795271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.994311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.032110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.986007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.879753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.830407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.137209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.838501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.867267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.915587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.109934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.156081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.094538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.980396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.944085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.234015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:30.938373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.968498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.016044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.207680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.257639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.190936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.083178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.042517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.327734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.039976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.076443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.279495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.315457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.357020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.287995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.180510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.140477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.428815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.154909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.195102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.417696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.434984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.470333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.377450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.281053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.257794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:39.525327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:31.259928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:32.302373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:33.524538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:34.534282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:35.576466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:36.477780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:37.378093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:10:38.359643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-21T21:11:07.394930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
DIRECTORIODIRECTORIO_HOGDIRECTORIO_PERNPCIP1NPCIP12NPCIP12ANPCIP12BNPCIP13ANPCIP13BNPCIP14NPCIP16ANPCIP16BNPCIP16CNPCIP16DNPCIP16ENPCIP16FNPCIP16GNPCIP16HNPCIP16INPCIP16JNPCIP2ANPCIP2BNPCIP2CNPCIP2DNPCIP2DANPCIP2ENPCIP2FNPCIP2GNPCIP3NPCIP4NPCIP5NPCIP6ANPCIP6BNPCIP6CNPCIP6DNPCIP6ENPCIP6FNPCIP6GNPCIP6HNPCIP7ANPCIP7BNPCIP7CNPCIP7DNPCIP7ENPCIP7FNPCIP7GNPCIP7HNPCIP8ANPCIP8BNPCIP8CNPCIP8DNPCIP8DENPCIP8ENPCIP8FNPCIP8GNPCIP8HNPCIP8INPCIP8JNPCIP8KORDENSECUENCIA_P
DIRECTORIO1.0001.0000.9990.0640.0410.0490.0500.0790.0430.0390.0610.0730.0280.0580.0660.1010.0480.0780.0630.0780.0460.0700.1350.0430.0340.0290.0180.0120.0480.0430.0300.0780.1140.0540.0150.0340.1090.0530.0010.0530.0640.1290.0440.0270.0530.0870.0160.0630.0830.0470.1410.0310.1220.0250.1020.0370.0590.0160.059-0.023-0.034
DIRECTORIO_HOG1.0001.0000.9990.0640.0410.0490.0500.0790.0430.0390.0610.0730.0280.0580.0660.1010.0480.0780.0630.0780.0460.0700.1350.0430.0340.0290.0180.0120.0480.0430.0300.0780.1140.0540.0150.0340.1090.0530.0010.0530.0640.1290.0440.0270.0530.0870.0160.0630.0830.0470.1410.0310.1220.0250.1020.0370.0590.0160.059-0.023-0.034
DIRECTORIO_PER0.9990.9991.0000.0520.0330.0320.0300.0790.0430.0070.0290.0560.0120.0420.0410.0710.0190.0630.0420.0560.0220.0500.1070.0270.0340.0120.0110.0000.0330.0200.0310.0720.0900.0480.0000.0320.0980.0470.0210.0130.0510.1060.0310.0320.0450.0540.0060.0480.0470.0260.1150.0110.0930.0090.0730.0000.0260.0080.040-0.022-0.034
NPCIP10.0640.0640.0521.0000.0770.1870.242-0.030-0.2200.0690.0600.0750.0610.2980.1630.3920.2530.2900.3360.1290.1020.2090.0400.171-0.3470.0650.0040.0000.2700.316-0.1010.5210.5860.2250.2160.1220.2250.1010.0100.0760.1900.1600.0320.0660.0490.0350.0160.1960.2110.0830.3720.0590.3380.4010.3050.1510.2300.0240.377-0.1290.020
NPCIP120.0410.0410.0330.0771.0001.0001.000NaNNaN1.0000.0080.0200.4370.4370.0580.1780.2080.1090.1510.0430.0080.2200.1210.0220.0170.0080.0240.0020.4490.2310.1330.0330.0650.0350.4310.0170.0750.0380.0180.0370.2000.1020.0180.0020.0270.1370.0040.1360.3360.4700.2210.2250.2460.2310.1590.0900.1920.0140.2260.3500.009
NPCIP12A0.0490.0490.0320.1871.0001.0000.8090.0960.0520.0000.0060.0190.0330.4620.0310.2050.2120.0840.1320.0160.0040.0340.0080.017-0.1030.0390.0140.0050.0650.4970.1880.0200.0230.0000.1020.0050.0350.0090.0140.0340.0270.0070.0410.0180.0060.0680.0040.0780.0840.0910.0390.0370.0590.0260.0390.0700.0710.0090.0500.0570.001
NPCIP12B0.0500.0500.0300.2421.0000.8091.000-0.110-0.0570.0000.0170.0260.0450.5610.0710.2600.2690.1120.1700.0410.0090.0550.0010.0250.0470.0110.0110.0000.0850.589-0.2010.0160.0530.0100.1140.0170.0490.0170.0190.0380.0430.0000.0000.0110.0180.0630.0030.0720.1000.1200.0800.0090.0830.0310.0570.0830.0860.0210.068-0.0690.001
NPCIP13A0.0790.0790.079-0.030NaN0.096-0.1101.000-0.0480.0000.0210.0570.0440.1580.0770.1230.1280.1100.0880.1000.0620.2350.0140.0270.1430.0140.0000.0230.1690.0740.0590.0610.1110.0670.0470.0430.0900.0530.0000.0270.2350.0240.0420.0310.0440.1810.0130.0320.0870.0830.1890.0620.1990.0930.1500.0360.1000.0120.219-0.135-0.001
NPCIP13B0.0430.0430.043-0.220NaN0.052-0.057-0.0481.0000.0000.0270.0100.0150.0160.0250.0320.0130.0360.0230.0190.0230.0310.0130.000-0.0460.0000.0000.0000.0300.0170.0100.0350.0790.0830.0170.0300.0580.0660.0000.0090.0260.0030.0100.0030.0090.0170.0000.0170.0280.0170.0730.0220.0720.0430.0830.0230.0490.0000.066-0.084-0.007
NPCIP140.0390.0390.0070.0691.0000.0000.0000.0000.0001.0000.1820.0440.4440.2740.0270.1820.1450.0290.0330.0310.0440.0710.0740.0380.1780.0360.0190.0000.0940.342-0.0070.1550.1060.0960.3530.0030.0000.0000.0230.0260.0810.0020.0190.0230.0460.0250.0000.0270.0740.0520.1070.1290.1040.0300.0770.1480.0440.0000.073-0.0730.004
NPCIP16A0.0610.0610.0290.0600.0080.0060.0170.0210.0270.1821.0000.0030.0840.0150.0080.0690.0710.0000.0190.0050.0070.0120.0130.0090.0480.0220.0000.0000.0290.0220.0410.0150.0540.0420.0400.0180.0000.0070.0040.0000.0050.0000.0230.0130.0320.0400.0030.0030.0020.0150.0530.0370.0400.0470.0560.0250.0140.0040.044-0.0140.003
NPCIP16B0.0730.0730.0560.0750.0200.0190.0260.0570.0100.0440.0031.0000.0160.0360.2510.0970.0730.1290.0910.2940.0250.0610.0100.004-0.0090.0000.0000.0010.0500.041-0.0220.0520.0580.0480.0130.0250.0330.0100.0000.0130.0540.0250.0000.0060.0050.0050.0020.0260.0390.0290.0960.0270.0750.0480.0920.0000.0360.0070.0430.0190.003
NPCIP16C0.0280.0280.0120.0610.4370.0330.0450.0440.0150.4440.0840.0161.0000.3330.0460.1780.1700.0690.1040.0310.0080.0740.0350.0030.0330.0070.0120.0000.1510.1830.0320.0160.0130.0080.1860.0020.0290.0150.0190.0240.0640.0320.0120.0050.0230.0530.0070.0380.1100.1390.0600.0710.0710.0680.0410.0450.0580.0000.0690.1160.006
NPCIP16D0.0580.0580.0420.2980.4370.4620.5610.1580.0160.2740.0150.0360.3331.0000.1030.3950.4200.1860.2690.0710.0260.1150.0300.0280.0010.0590.0170.0000.2000.666-0.1820.0170.0680.0030.2710.0180.0580.0210.0280.0560.1120.0210.0150.0070.0070.0880.0060.0940.2200.3000.1400.0800.1520.0450.0940.1030.1190.0180.1360.0200.006
NPCIP16E0.0660.0660.0410.1630.0580.0310.0710.0770.0250.0270.0080.2510.0460.1031.0000.2220.1980.3200.2450.3850.0320.0670.0430.009-0.1050.0150.0130.0010.0710.114-0.0050.0950.0940.0660.0040.0530.0660.0600.0000.0190.0640.0560.0210.0100.0170.0080.0020.0520.0900.0750.1430.0270.1060.0930.1200.0410.0710.0050.091-0.0160.011
NPCIP16F0.1010.1010.0710.3920.1780.2050.2600.1230.0320.1820.0690.0970.1780.3950.2221.0000.5780.3460.4460.1620.0210.1340.0340.018-0.1090.0280.0000.0020.1470.429-0.0270.1550.1960.0730.0380.0520.1210.0660.0070.0250.1420.0670.0370.0220.0490.0780.0050.1130.3440.2150.2170.0810.2060.1400.1580.1120.1470.0110.194-0.0520.006
NPCIP16G0.0480.0480.0190.2530.2080.2120.2690.1280.0130.1450.0710.0730.1700.4200.1980.5781.0000.3790.5250.1440.0060.0790.0180.011-0.0630.0220.0010.0070.0660.425-0.0240.0580.0720.0110.0870.0300.0590.0400.0090.0220.0960.0290.0530.0280.0550.0670.0000.0650.1960.3420.1250.0840.1090.0500.0600.1460.1020.0120.089-0.074-0.004
NPCIP16H0.0780.0780.0630.2900.1090.0840.1120.1100.0360.0290.0000.1290.0690.1860.3200.3460.3791.0000.5750.2900.0460.0860.0520.022-0.1190.0080.0000.0050.1200.209-0.0040.1280.2020.1030.0160.0910.1290.0870.0020.0290.0970.0780.0300.0040.0360.0420.0040.0920.1470.1530.2430.0460.1930.1530.1790.1180.1570.0040.160-0.0610.006
NPCIP16I0.0630.0630.0420.3360.1510.1320.1700.0880.0230.0330.0190.0910.1040.2690.2450.4460.5250.5751.0000.2180.0410.0810.0610.026-0.1660.0070.0040.0010.1460.300-0.0120.1240.2190.0940.0450.0920.1450.0870.0010.0390.1000.0890.0340.0130.0510.0650.0060.1110.1920.2260.2530.0810.2060.1870.1580.1700.1740.0110.167-0.1150.003
NPCIP16J0.0780.0780.0560.1290.0430.0160.0410.1000.0190.0310.0050.2940.0310.0710.3850.1620.1440.2900.2181.0000.0160.0710.0420.008-0.0420.0000.0080.0010.0630.074-0.0310.0820.0880.0620.0120.0390.0560.0380.0000.0060.0670.0520.0130.0060.0050.0110.0030.0350.0680.0530.1220.0340.1010.0800.1180.0330.0660.0040.0740.0000.006
NPCIP2A0.0460.0460.0220.1020.0080.0040.0090.0620.0230.0440.0070.0250.0080.0260.0320.0210.0060.0460.0410.0161.0000.2080.0430.289-0.0370.1840.0700.0490.1040.069-0.0570.0780.1650.0560.0060.0340.0670.0310.0050.4390.1770.0280.0720.1290.0960.0240.0550.0340.0000.0000.0520.0210.0270.0830.0420.0370.0460.0020.067-0.0210.016
NPCIP2B0.0700.0700.0500.2090.2200.0340.0550.2350.0310.0710.0120.0610.0740.1150.0670.1340.0790.0860.0810.0710.2081.0000.0950.046-0.0310.0000.0110.0000.6050.1320.0920.1110.1350.0280.1470.0160.0940.0530.0020.0540.8180.0940.0080.0140.0310.1390.0060.0820.1960.1920.2980.2690.3530.0580.2430.0590.1630.0290.5360.2440.003
NPCIP2C0.1350.1350.1070.0400.1210.0080.0010.0140.0130.0740.0130.0100.0350.0300.0430.0340.0180.0520.0610.0420.0430.0951.0000.030-0.0920.0800.0980.0000.3340.035-0.0990.0230.0490.0680.0420.0810.0460.0470.0000.0440.1000.7840.0860.0340.0530.0070.0000.0060.0420.0550.0800.1310.1190.1590.0710.0110.0380.0140.161-0.1960.005
NPCIP2D0.0430.0430.0270.1710.0220.0170.0250.0270.0000.0380.0090.0040.0030.0280.0090.0180.0110.0220.0260.0080.2890.0460.0301.000NaN0.1560.0700.0150.0820.084-0.0250.0270.0860.0170.0000.0080.0270.0050.0050.1740.0310.0210.1410.4280.0990.0180.0470.0080.0050.0000.0560.0320.0560.0160.0230.0000.0140.0270.050-0.007-0.009
NPCIP2DA0.0340.0340.034-0.3470.017-0.1030.0470.143-0.0460.1780.048-0.0090.0330.001-0.105-0.109-0.063-0.119-0.166-0.042-0.037-0.031-0.092NaN1.0000.0990.0200.0000.0950.0430.2000.0730.0940.0710.0780.0160.0280.0000.0000.0590.0550.0230.0000.0000.0000.0000.0000.0520.0350.0290.0320.0390.0650.1620.0180.0000.0190.0000.0440.0930.004
NPCIP2E0.0290.0290.0120.0650.0080.0390.0110.0140.0000.0360.0220.0000.0070.0590.0150.0280.0220.0080.0070.0000.1840.0000.0800.1560.0991.0000.1060.0420.0680.089-0.0220.0250.0260.0220.0150.0510.0560.0590.0130.1520.0000.0730.1710.1080.4440.0650.0030.0240.0080.0130.0310.0180.0390.0080.0120.0260.0140.0090.050-0.012-0.004
NPCIP2F0.0180.0180.0110.0040.0240.0140.0110.0000.0000.0190.0000.0000.0120.0170.0130.0000.0010.0000.0040.0080.0700.0110.0980.0700.0200.1061.0000.0410.0510.0220.0380.0120.0000.0140.0150.0160.0000.0110.0000.0830.0130.0940.1510.0750.0490.0000.0040.0030.0040.0020.0180.0290.0240.0260.0060.0000.0000.0000.028-0.033-0.002
NPCIP2G0.0120.0120.0000.0000.0020.0050.0000.0230.0000.0000.0000.0010.0000.0000.0010.0020.0070.0050.0010.0010.0490.0000.0000.0150.0000.0420.0411.0000.0040.0030.0460.0090.0000.0060.0050.0070.0090.0000.0190.0480.0020.0000.0320.0140.0300.0200.3480.0000.0020.0000.0000.0100.0000.0000.0040.0010.0020.0010.0000.005-0.001
NPCIP30.0480.0480.0330.2700.4490.0650.0850.1690.0300.0940.0290.0500.1510.2000.0710.1470.0660.1200.1460.0630.1040.6050.3340.0820.0950.0680.0510.0041.0000.165-0.2020.0990.2300.0490.2700.0550.1320.0480.0080.0100.5780.3340.0240.0420.0250.1670.0110.1360.2850.2960.4550.2520.5320.4450.3750.0930.2600.2130.7860.2060.011
NPCIP40.0430.0430.0200.3160.2310.4970.5890.0740.0170.3420.0220.0410.1830.6660.1140.4290.4250.2090.3000.0740.0690.1320.0350.0840.0430.0890.0220.0030.1651.000NaN0.0890.1870.0510.0340.0430.0960.0350.0180.1460.1430.0180.0450.0330.0140.0820.0140.1440.1760.1890.1920.0470.1940.1590.1420.1580.1560.0210.205-0.0660.005
NPCIP50.0300.0300.031-0.1010.1330.188-0.2010.0590.010-0.0070.041-0.0220.032-0.182-0.005-0.027-0.024-0.004-0.012-0.031-0.0570.092-0.099-0.0250.200-0.0220.0380.046-0.202NaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1490.012
NPCIP6A0.0780.0780.0720.5210.0330.0200.0160.0610.0350.1550.0150.0520.0160.0170.0950.1550.0580.1280.1240.0820.0780.1110.0230.0270.0730.0250.0120.0090.0990.0890.0001.0000.0270.1090.1580.0690.1000.0640.0080.0150.1480.1040.0000.0320.0320.0070.0090.1210.1090.0220.1790.0490.1640.2010.1590.0570.1040.0100.200-0.0680.009
NPCIP6B0.1140.1140.0900.5860.0650.0230.0530.1110.0790.1060.0540.0580.0130.0680.0940.1960.0720.2020.2190.0880.1650.1350.0490.0860.0940.0260.0000.0000.2300.1870.0000.0271.0000.1950.0540.1410.2790.1250.0040.0850.1710.1350.0060.0030.0400.0970.0150.1730.2000.1070.3670.0620.3440.2870.3120.1690.2550.0150.327-0.0110.021
NPCIP6C0.0540.0540.0480.2250.0350.0000.0100.0670.0830.0960.0420.0480.0080.0030.0660.0730.0110.1030.0940.0620.0560.0280.0680.0170.0710.0220.0140.0060.0490.0510.0000.1090.1951.0000.1300.1630.1760.1390.0040.0310.0550.1110.0120.0080.0330.0350.0030.0670.0500.0090.1450.0340.1190.1410.1510.0790.1110.0060.095-0.0110.010
NPCIP6D0.0150.0150.0000.2160.4310.1020.1140.0470.0170.3530.0400.0130.1860.2710.0040.0380.0870.0160.0450.0120.0060.1470.0420.0000.0780.0150.0150.0050.2700.0340.0000.1580.0540.1301.0000.0110.0450.0290.0390.0390.1020.0660.0280.0000.0430.1050.0030.0710.1790.2780.0940.0970.1110.1430.0650.0850.1040.0040.0950.176-0.001
NPCIP6E0.0340.0340.0320.1220.0170.0050.0170.0430.0300.0030.0180.0250.0020.0180.0530.0520.0300.0910.0920.0390.0340.0160.0810.0080.0160.0510.0160.0070.0550.0430.0000.0690.1410.1630.0111.0000.2890.3330.0010.0230.0300.1070.0420.0430.0830.0510.0000.0550.0450.0320.1040.0440.0800.1010.0910.1000.0980.0070.057-0.0300.001
NPCIP6F0.1090.1090.0980.2250.0750.0350.0490.0900.0580.0000.0000.0330.0290.0580.0660.1210.0590.1290.1450.0560.0670.0940.0460.0270.0280.0560.0000.0090.1320.0960.0000.1000.2790.1760.0450.2891.0000.3190.0050.0560.1100.0830.0560.0500.1630.1750.0060.1160.1190.0890.2590.0160.2400.1290.2360.1720.2510.0110.1930.0560.012
NPCIP6G0.0530.0530.0470.1010.0380.0090.0170.0530.0660.0000.0070.0100.0150.0210.0600.0660.0400.0870.0870.0380.0310.0530.0470.0050.0000.0590.0110.0000.0480.0350.0000.0640.1250.1390.0290.3330.3191.0000.0070.0220.0570.0710.0980.1030.1200.0960.0000.0490.0610.0530.1250.0180.1010.0700.1180.0960.1360.0060.0770.011-0.001
NPCIP6H0.0010.0010.0210.0100.0180.0140.0190.0000.0000.0230.0040.0000.0190.0280.0000.0070.0090.0020.0010.0000.0050.0020.0000.0050.0000.0130.0000.0190.0080.0180.0000.0080.0040.0040.0390.0010.0050.0071.0000.0330.0000.0000.0040.0170.0080.0000.0610.0090.0120.0190.0030.0070.0040.0050.0000.0000.0000.0360.000-0.0010.001
NPCIP7A0.0530.0530.0130.0760.0370.0340.0380.0270.0090.0260.0000.0130.0240.0560.0190.0250.0220.0290.0390.0060.4390.0540.0440.1740.0590.1520.0830.0480.0100.1460.0000.0150.0850.0310.0390.0230.0560.0220.0331.0000.0330.0400.1630.1250.1460.0380.1370.0600.0370.0390.0680.0260.0610.0500.0500.0690.0630.0110.021-0.0050.007
NPCIP7B0.0640.0640.0510.1900.2000.0270.0430.2350.0260.0810.0050.0540.0640.1120.0640.1420.0960.0970.1000.0670.1770.8180.1000.0310.0550.0000.0130.0020.5780.1430.0000.1480.1710.0550.1020.0300.1100.0570.0000.0331.0000.0540.0230.0040.0550.1680.0140.0980.1940.1860.2910.1920.3280.0250.2270.0700.1590.0290.5100.1980.004
NPCIP7C0.1290.1290.1060.1600.1020.0070.0000.0240.0030.0020.0000.0250.0320.0210.0560.0670.0290.0780.0890.0520.0280.0940.7840.0210.0230.0730.0940.0000.3340.0180.0000.1040.1350.1110.0660.1070.0830.0710.0000.0400.0541.0000.1170.0730.0690.0080.0000.0470.0120.0280.0020.0950.0430.1930.0100.0290.0070.0130.081-0.1760.004
NPCIP7D0.0440.0440.0310.0320.0180.0410.0000.0420.0100.0190.0230.0000.0120.0150.0210.0370.0530.0300.0340.0130.0720.0080.0860.1410.0000.1710.1510.0320.0240.0450.0000.0000.0060.0120.0280.0420.0560.0980.0040.1630.0230.1171.0000.3250.2780.1180.0020.0030.0340.0410.0050.0490.0010.0050.0220.0230.0430.0040.007-0.0030.000
NPCIP7E0.0270.0270.0320.0660.0020.0180.0110.0310.0030.0230.0130.0060.0050.0070.0100.0220.0280.0040.0130.0060.1290.0140.0340.4280.0000.1080.0750.0140.0420.0330.0000.0320.0030.0080.0000.0430.0500.1030.0170.1250.0040.0730.3251.0000.1900.0900.0000.0130.0200.0180.0100.0410.0050.0050.0270.0160.0280.0110.005-0.003-0.002
NPCIP7F0.0530.0530.0450.0490.0270.0060.0180.0440.0090.0460.0320.0050.0230.0070.0170.0490.0550.0360.0510.0050.0960.0310.0530.0990.0000.4440.0490.0300.0250.0140.0000.0320.0400.0330.0430.0830.1630.1200.0080.1460.0550.0690.2780.1901.0000.2730.0000.0420.0620.0560.0580.0450.0530.0330.0510.0990.1030.0050.0320.0110.003
NPCIP7G0.0870.0870.0540.0350.1370.0680.0630.1810.0170.0250.0400.0050.0530.0880.0080.0780.0670.0420.0650.0110.0240.1390.0070.0180.0000.0650.0000.0200.1670.0820.0000.0070.0970.0350.1050.0510.1750.0960.0000.0380.1680.0080.1180.0900.2731.0000.0050.1010.1420.1270.1650.0410.1870.0250.1430.1350.1780.0010.1790.0960.009
NPCIP7H0.0160.0160.0060.0160.0040.0040.0030.0130.0000.0000.0030.0020.0070.0060.0020.0050.0000.0040.0060.0030.0550.0060.0000.0470.0000.0030.0040.3480.0110.0140.0000.0090.0150.0030.0030.0000.0060.0000.0610.1370.0140.0000.0020.0000.0000.0051.0000.0110.0110.0080.0130.0160.0120.0040.0080.0060.0060.0230.005-0.000-0.006
NPCIP8A0.0630.0630.0480.1960.1360.0780.0720.0320.0170.0270.0030.0260.0380.0940.0520.1130.0650.0920.1110.0350.0340.0820.0060.0080.0520.0240.0030.0000.1360.1440.0000.1210.1730.0670.0710.0550.1160.0490.0090.0600.0980.0470.0030.0130.0420.1010.0111.0000.2860.1650.2360.0770.2220.1790.2080.1810.2580.0280.1770.0220.010
NPCIP8B0.0830.0830.0470.2110.3360.0840.1000.0870.0280.0740.0020.0390.1100.2200.0900.3440.1960.1470.1920.0680.0000.1960.0420.0050.0350.0080.0040.0020.2850.1760.0000.1090.2000.0500.1790.0450.1190.0610.0120.0370.1940.0120.0340.0200.0620.1420.0110.2861.0000.4150.3150.1150.3120.0450.2350.1370.2410.0200.2500.1260.020
NPCIP8C0.0470.0470.0260.0830.4700.0910.1200.0830.0170.0520.0150.0290.1390.3000.0750.2150.3420.1530.2260.0530.0000.1920.0550.0000.0290.0130.0020.0000.2960.1890.0000.0220.1070.0090.2780.0320.0890.0530.0190.0390.1860.0280.0410.0180.0560.1270.0080.1650.4151.0000.2790.1250.2680.0640.1790.1630.2010.0200.2020.1610.004
NPCIP8D0.1410.1410.1150.3720.2210.0390.0800.1890.0730.1070.0530.0960.0600.1400.1430.2170.1250.2430.2530.1220.0520.2980.0800.0560.0320.0310.0180.0000.4550.1920.0000.1790.3670.1450.0940.1040.2590.1250.0030.0680.2910.0020.0050.0100.0580.1650.0130.2360.3150.2791.0001.0000.6780.1950.5160.2360.3860.0290.4720.1690.020
NPCIP8DE0.0310.0310.0110.0590.2250.0370.0090.0620.0220.1290.0370.0270.0710.0800.0270.0810.0840.0460.0810.0340.0210.2690.1310.0320.0390.0180.0290.0100.2520.0470.0000.0490.0620.0340.0970.0440.0160.0180.0070.0260.1920.0950.0490.0410.0450.0410.0160.0770.1150.1251.0001.0001.0000.2150.0680.0700.0540.0220.2130.2380.009
NPCIP8E0.1220.1220.0930.3380.2460.0590.0830.1990.0720.1040.0400.0750.0710.1520.1060.2060.1090.1930.2060.1010.0270.3530.1190.0560.0650.0390.0240.0000.5320.1940.0000.1640.3440.1190.1110.0800.2400.1010.0040.0610.3280.0430.0010.0050.0530.1870.0120.2220.3120.2680.6781.0001.0000.1580.5190.2220.3820.0290.5100.2340.022
NPCIP8F0.0250.0250.0090.4010.2310.0260.0310.0930.0430.0300.0470.0480.0680.0450.0930.1400.0500.1530.1870.0800.0830.0580.1590.0160.1620.0080.0260.0000.4450.1590.0000.2010.2870.1410.1430.1010.1290.0700.0050.0500.0250.1930.0050.0050.0330.0250.0040.1790.0450.0640.1950.2150.1581.0000.2710.2120.2050.0370.111-0.2520.009
NPCIP8G0.1020.1020.0730.3050.1590.0390.0570.1500.0830.0770.0560.0920.0410.0940.1200.1580.0600.1790.1580.1180.0420.2430.0710.0230.0180.0120.0060.0040.3750.1420.0000.1590.3120.1510.0650.0910.2360.1180.0000.0500.2270.0100.0220.0270.0510.1430.0080.2080.2350.1790.5160.0680.5190.2711.0000.3000.4600.0180.4280.1680.018
NPCIP8H0.0370.0370.0000.1510.0900.0700.0830.0360.0230.1480.0250.0000.0450.1030.0410.1120.1460.1180.1700.0330.0370.0590.0110.0000.0000.0260.0000.0010.0930.1580.0000.0570.1690.0790.0850.1000.1720.0960.0000.0690.0700.0290.0230.0160.0990.1350.0060.1810.1370.1630.2360.0700.2220.2120.3001.0000.4490.0300.177-0.0170.004
NPCIP8I0.0590.0590.0260.2300.1920.0710.0860.1000.0490.0440.0140.0360.0580.1190.0710.1470.1020.1570.1740.0660.0460.1630.0380.0140.0190.0140.0000.0020.2600.1560.0000.1040.2550.1110.1040.0980.2510.1360.0000.0630.1590.0070.0430.0280.1030.1780.0060.2580.2410.2010.3860.0540.3820.2050.4600.4491.0000.0170.3290.1150.011
NPCIP8J0.0160.0160.0080.0240.0140.0090.0210.0120.0000.0000.0040.0070.0000.0180.0050.0110.0120.0040.0110.0040.0020.0290.0140.0270.0000.0090.0000.0010.2130.0210.0000.0100.0150.0060.0040.0070.0110.0060.0360.0110.0290.0130.0040.0110.0050.0010.0230.0280.0200.0200.0290.0220.0290.0370.0180.0300.0171.0000.0340.005-0.008
NPCIP8K0.0590.0590.0400.3770.2260.0500.0680.2190.0660.0730.0440.0430.0690.1360.0910.1940.0890.1600.1670.0740.0670.5360.1610.0500.0440.0500.0280.0000.7860.2050.0000.2000.3270.0950.0950.0570.1930.0770.0000.0210.5100.0810.0070.0050.0320.1790.0050.1770.2500.2020.4720.2130.5100.1110.4280.1770.3290.0341.0000.2430.009
ORDEN-0.023-0.023-0.022-0.1290.3500.057-0.069-0.135-0.084-0.073-0.0140.0190.1160.020-0.016-0.052-0.074-0.061-0.1150.000-0.0210.244-0.196-0.0070.093-0.012-0.0330.0050.206-0.0660.149-0.068-0.011-0.0110.176-0.0300.0560.011-0.001-0.0050.198-0.176-0.003-0.0030.0110.096-0.0000.0220.1260.1610.1690.2380.234-0.2520.168-0.0170.1150.0050.2431.000-0.012
SECUENCIA_P-0.034-0.034-0.0340.0200.0090.0010.001-0.001-0.0070.0040.0030.0030.0060.0060.0110.006-0.0040.0060.0030.0060.0160.0030.005-0.0090.004-0.004-0.002-0.0010.0110.0050.0120.0090.0210.010-0.0010.0010.012-0.0010.0010.0070.0040.0040.000-0.0020.0030.009-0.0060.0100.0200.0040.0200.0090.0220.0090.0180.0040.011-0.0080.009-0.0121.000

Missing values

2024-04-21T21:10:39.850663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:10:41.402260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DIRECTORIODIRECTORIO_HOGDIRECTORIO_PERSECUENCIA_PORDENNPCIP1NPCIP2ANPCIP2BNPCIP2CNPCIP2DNPCIP2ENPCIP2FNPCIP2GNPCIP2DANPCIP3NPCIP4NPCIP5NPCIP6ANPCIP6BNPCIP6CNPCIP6DNPCIP6ENPCIP6FNPCIP6GNPCIP6HNPCIP7ANPCIP7BNPCIP7CNPCIP7DNPCIP7ENPCIP7FNPCIP7GNPCIP7HNPCIP8ANPCIP8BNPCIP8CNPCIP8DNPCIP8ENPCIP8FNPCIP8GNPCIP8HNPCIP8INPCIP8KNPCIP8JNPCIP8DENPCIP11ANPCIP11BNPCIP11CNPCIP11DNPCIP12NPCIP12ANPCIP12BNPCIP13A1NPCIP13B1NPCIP13ANPCIP13BNPCIP14NPCIP14ANPCIP14BNPCIP16ANPCIP16BNPCIP16CNPCIP16DNPCIP16ENPCIP16FNPCIP16GNPCIP16HNPCIP16INPCIP16JNPCIP17ANPCIP17BNPCIP17CNPCIP17DNPCIP17ENPCIP17FNPCIP17GNPCIP17HNPCIP17INPCIP17JNPCIP17KNPCIP17LNPCIP17MFEX_C
0166238.0166238116623811115NaNNaNNaNNaNNaNNaNNaNNaNNaN51.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.02.01.0NaN35000.0NaNNaNNaNNaN1.02.01.02.02.02.02.02.02.02.01.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1,69849246231156
1166238.0166238116623812125NaNNaNNaNNaNNaNNaNNaNNaNNaN51.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.02.01.0NaN25000.0NaNNaNNaNNaN1.02.01.02.02.02.02.02.02.02.01.0NaN1.0NaNNaNNaNNaNNaNNaN1.0NaNNaNNaN1,69849246231156
2220102.0220102122010211115NaNNaNNaNNaNNaNNaNNaNNaNNaN3NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.02.02.01.02.02.02.02.02.02.02.02.02.01.01.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.02.02.02.02.02.02.01.0NaN1.0NaNNaNNaNNaNNaN1.01.0NaNNaNNaN6,74441212899351
3220102.0220102122010212125NaNNaNNaNNaNNaNNaNNaNNaNNaN2NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.02.01.02.02.02.02.02.02.02.02.02.0NaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.02.02.02.02.02.02.0NaNNaN1.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaN6,74441212899351
4220102.0220102122010213135NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.01.01.02.02.01.02.02.02.02.02.02.0NaN1.01.0NaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.02.01.01.02.02.02.01.0NaN1.0NaNNaN1.01.0NaN1.0NaNNaNNaNNaN6,74441212899351
5220102.0220102122010214145NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.02.01.02.02.02.02.02.02.02.02.02.0NaN1.01.0NaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.02.01.01.02.02.02.01.0NaN1.01.0NaN1.01.0NaNNaNNaNNaNNaNNaN6,74441212899351
6220385.0220385122038511115NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.01.02.02.01.01.01.02.02.02.02.02.02.02.02.03.0NaNNaNNaN1.012.01.01.0NaN47000.0NaNNaNNaNNaN1.01.01.02.02.01.02.02.02.02.01.0NaN1.0NaN1.01.01.0NaN1.0NaNNaNNaNNaN1,80463242698892
7220385.0220385122038512125NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.01.02.02.01.01.01.02.02.02.02.01.01.02.02.03.0NaNNaNNaN1.012.01.01.0NaN6000.0NaNNaNNaNNaN1.02.01.01.02.02.01.02.02.02.01.0NaN1.01.01.0NaN1.0NaNNaNNaNNaNNaNNaN1,80463242698892
8220385.0220385122038513135NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.02.01.02.02.01.02.01.02.02.02.03.0NaN1.0NaNNaN2NaNNaNNaNNaNNaNNaN1.0NaN1.01.02.02.02.02.02.02.02.02.02.01.0NaN1.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaN1,80463242698892
9222175.02221751222175111111.01.02.02.02.02.02.0NaN1.01NaN2.01.02.01.02.02.02.02.01.01.02.02.02.02.02.02.01.01.01.02.01.01.01.02.01.01.02.0NaN1.0NaNNaNNaN11.01.0NaN1.0NaN75000.0NaNNaNNaN2.02.01.01.02.01.01.02.01.02.0NaNNaN1.01.01.01.01.0NaNNaNNaNNaNNaNNaN164,802119304977
DIRECTORIODIRECTORIO_HOGDIRECTORIO_PERSECUENCIA_PORDENNPCIP1NPCIP2ANPCIP2BNPCIP2CNPCIP2DNPCIP2ENPCIP2FNPCIP2GNPCIP2DANPCIP3NPCIP4NPCIP5NPCIP6ANPCIP6BNPCIP6CNPCIP6DNPCIP6ENPCIP6FNPCIP6GNPCIP6HNPCIP7ANPCIP7BNPCIP7CNPCIP7DNPCIP7ENPCIP7FNPCIP7GNPCIP7HNPCIP8ANPCIP8BNPCIP8CNPCIP8DNPCIP8ENPCIP8FNPCIP8GNPCIP8HNPCIP8INPCIP8KNPCIP8JNPCIP8DENPCIP11ANPCIP11BNPCIP11CNPCIP11DNPCIP12NPCIP12ANPCIP12BNPCIP13A1NPCIP13B1NPCIP13ANPCIP13BNPCIP14NPCIP14ANPCIP14BNPCIP16ANPCIP16BNPCIP16CNPCIP16DNPCIP16ENPCIP16FNPCIP16GNPCIP16HNPCIP16INPCIP16JNPCIP17ANPCIP17BNPCIP17CNPCIP17DNPCIP17ENPCIP17FNPCIP17GNPCIP17HNPCIP17INPCIP17JNPCIP17KNPCIP17LNPCIP17MFEX_C
2780123006810.0300681013006810131311.02.01.02.02.02.02.0NaN2.01NaN1.02.02.01.02.01.02.02.01.02.01.02.02.02.02.02.01.01.01.02.02.01.02.01.01.02.02.03.0NaN1.0NaNNaN12.01.0NaN1.0NaN25000.0NaNNaNNaN1.02.01.01.01.01.01.01.01.02.01.01.01.01.01.01.01.0NaNNaNNaNNaNNaNNaN15,5533986527622
2780133006810.0300681013006810141411.02.01.02.02.02.02.0NaN2.01NaN1.02.02.01.02.01.02.02.01.02.01.02.02.02.02.02.01.01.01.02.02.02.02.01.01.02.02.03.0NaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.01.01.01.01.01.02.01.01.01.01.01.01.01.01.01.01.0NaNNaNNaN15,5533986527622
2780143006810.0300681013006810151521.02.02.02.02.02.02.0NaN2.01NaN1.01.02.01.02.01.02.02.01.02.02.02.02.01.02.02.01.01.01.02.02.01.02.01.02.02.02.03.0NaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.02.01.01.02.02.01.02.01.02.01.0NaN1.01.01.0NaN1.0NaNNaNNaNNaNNaNNaN15,5533986527622
2780153006810.0300681013006810161611.02.02.02.02.02.02.0NaN2.01NaN1.02.02.01.02.01.02.02.01.02.02.02.02.02.02.02.01.02.02.02.02.02.02.01.02.02.02.03.0NaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15,5533986527622
2780163006811.0300681113006811111131.02.02.02.02.02.02.0NaN3.01NaN1.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.01.01.01.02.02.02.02.01.02.02.0NaNNaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.01.01.01.02.02.01.02.02.02.01.01.01.01.01.0NaN1.0NaN1.01.01.0NaNNaN15,0488158778498
2780173006811.0300681113006811121241.02.02.02.02.02.02.0NaN3.01NaN1.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.01.01.02.02.02.02.02.02.02.02.03.0NaN1.0NaNNaN12.01.01.0NaN0.0NaNNaNNaNNaN1.01.01.01.02.02.01.02.02.02.01.01.01.01.0NaNNaN1.0NaN1.01.01.0NaNNaN15,0488158778498
2780183006811.0300681113006811131311.01.02.02.02.02.02.0NaN1.01NaN1.02.02.01.02.01.02.02.01.01.02.02.02.02.02.02.01.01.01.01.01.02.02.01.01.01.02.0NaNNaN1.0NaNNaN12.01.0NaN1.0NaN45000.0NaNNaNNaN1.01.01.01.02.01.01.02.01.02.01.01.01.01.01.01.01.0NaN1.01.01.0NaNNaN15,0488158778498
2780193006812.030068121300681211115NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.02.01.02.02.02.02.02.02.02.02.01.01.0NaNNaNNaN12.01.01.0NaN20000.0NaNNaNNaNNaN1.02.01.01.02.02.02.02.02.02.01.0NaN1.01.01.0NaNNaNNaNNaN1.0NaNNaNNaN16,3462870407952
2780203006812.030068121300681212125NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.02.01.02.02.02.02.02.01.02.02.01.01.0NaNNaNNaN12.01.01.0NaN15000.0NaNNaNNaNNaN1.02.01.01.02.01.01.02.01.02.01.0NaN1.01.01.0NaN1.0NaN1.01.0NaNNaNNaN16,3462870407952
2780213006812.030068121300681213135NaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN2.02.02.01.02.02.02.02.01.02.02.02.02.02.02.02.01.01.01.02.02.02.02.02.02.02.02.01.01.01.0NaNNaN12.01.01.0NaN10000.0NaNNaNNaNNaN1.02.01.01.02.01.01.02.02.02.01.0NaN1.01.01.0NaNNaNNaN1.0NaNNaNNaNNaN16,3462870407952